Combined prediction model based on partial least squares regression and its application to near infrared spectroscopy quantitative analysis

Aiming at the near infrared spectra (NIR) on local effect sensitivity, numerous predictor variables with serious multicollinearity and having nonlinear quantitative relationship with the chemical compositions from the spectral data, a novel ensemble model, termed as ensemble model based on the subbaggin technique and quadratic partial least squares regression ( E-S-QPLSR), was constructed. Firstly, a quite quantity of forecasting sub-models had been established by using the non-linear quadratic partial least squares regression (QPLSR) method and the subagging algorithm, that was a subsampling technique without replacement from the training samples. Then, based on the groups of the training samples forecasting values from the above sub-models, all of the sub-model weighting cocefficients were calculated by using the linear PLSR algorithm. Finally, the application to the corn samples water content modeling of the proposed E-S-QPLSR method was presented in comparison with some other methods. The E-S-QPLSR method not only holds on fine learning ability, but also improves the prediction performance and steady capability.